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      <h1 class="site-logo" id="site-title">深入浅出PyTorch</h1>
      
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  <a class="reference internal" href="../%E7%AC%AC%E4%B8%80%E7%AB%A0/index.html">
   第一章：PyTorch的简介和安装
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     1.1 PyTorch简介
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     1.2 PyTorch的安装
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     1.3 PyTorch相关资源
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   第二章：PyTorch基础知识
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     2.1 张量
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     2.2 自动求导
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     2.3 并行计算简介
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   第三章：PyTorch的主要组成模块
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     3.1 思考：完成深度学习的必要部分
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     3.2 基本配置
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     3.3 数据读入
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     3.4 模型构建
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     3.5 模型初始化
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     3.6 损失函数
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     3.7 训练和评估
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     3.8 可视化
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     3.9 Pytorch优化器
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   第四章：PyTorch基础实战
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     基础实战——FashionMNIST时装分类
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   第五章：PyTorch模型定义
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     5.1 PyTorch模型定义的方式
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    <a class="reference internal" href="../%E7%AC%AC%E4%BA%94%E7%AB%A0/5.2%20%E5%88%A9%E7%94%A8%E6%A8%A1%E5%9E%8B%E5%9D%97%E5%BF%AB%E9%80%9F%E6%90%AD%E5%BB%BA%E5%A4%8D%E6%9D%82%E7%BD%91%E7%BB%9C.html">
     5.2 利用模型块快速搭建复杂网络
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     5.3 PyTorch修改模型
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    <a class="reference internal" href="../%E7%AC%AC%E4%BA%94%E7%AB%A0/5.4%20PyTorh%E6%A8%A1%E5%9E%8B%E4%BF%9D%E5%AD%98%E4%B8%8E%E8%AF%BB%E5%8F%96.html">
     5.4 PyTorch模型保存与读取
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   第六章：PyTorch进阶训练技巧
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     6.1 自定义损失函数
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     6.2 动态调整学习率
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     6.3 模型微调-torchvision
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     6.3 模型微调 - timm
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     6.4 半精度训练
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    <a class="reference internal" href="../%E7%AC%AC%E5%85%AD%E7%AB%A0/6.5%20%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA-imgaug.html">
     6.5 数据增强-imgaug
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    <a class="reference internal" href="../%E7%AC%AC%E5%85%AD%E7%AB%A0/6.6%20%E4%BD%BF%E7%94%A8argparse%E8%BF%9B%E8%A1%8C%E8%B0%83%E5%8F%82.html">
     6.6 使用argparse进行调参
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     PyTorch模型定义与进阶训练技巧
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   第七章：PyTorch可视化
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     7.1 可视化网络结构
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     7.2 CNN可视化
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     7.3 使用TensorBoard可视化训练过程
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   第八章：PyTorch生态简介
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     8.1 本章简介
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     8.2 torchvision
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     8.3 PyTorchVideo简介
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     8.4 torchtext简介
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     transforms实战
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   8.4.2 torchtext的安装
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  <section class="tex2jax_ignore mathjax_ignore" id="torchtext">
<h1>8.4 torchtext简介<a class="headerlink" href="#torchtext" title="永久链接至标题">#</a></h1>
<p>本节我们来介绍PyTorch官方用于自然语言处理（NLP）的工具包torchtext。自然语言处理也是深度学习的一大应用场景，近年来随着大规模预训练模型的应用，深度学习在人机对话、机器翻译等领域的取得了非常好的效果，也使得NLP相关的深度学习模型获得了越来越多的关注。</p>
<p>由于NLP和CV在数据预处理中的不同，因此NLP的工具包torchtext和torchvision等CV相关工具包也有一些功能上的差异，如：</p>
<ul class="simple">
<li><p>数据集（dataset）定义方式不同</p></li>
<li><p>数据预处理工具</p></li>
<li><p>没有琳琅满目的model zoo</p></li>
</ul>
<p>本节介绍参考了<a class="reference external" href="https://github.com/atnlp/torchtext-summary">atnlp的Github</a>，在此致谢！</p>
<section id="id1">
<h2>8.4.1 torchtext的主要组成部分<a class="headerlink" href="#id1" title="永久链接至标题">#</a></h2>
<p>torchtext可以方便的对文本进行预处理，例如截断补长、构建词表等。torchtext主要包含了以下的主要组成部分：</p>
<ul class="simple">
<li><p>数据处理工具 torchtext.data.functional、torchtext.data.utils</p></li>
<li><p>数据集 torchtext.data.datasets</p></li>
<li><p>词表工具 torchtext.vocab</p></li>
<li><p>评测指标 torchtext.metrics</p></li>
</ul>
</section>
<section id="id2">
<h2>8.4.2 torchtext的安装<a class="headerlink" href="#id2" title="永久链接至标题">#</a></h2>
<p>torchtext可以直接使用pip进行安装：</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install torchtext
</pre></div>
</div>
</section>
<section id="id3">
<h2>8.4.3 构建数据集<a class="headerlink" href="#id3" title="永久链接至标题">#</a></h2>
<ul class="simple">
<li><p><strong>Field及其使用</strong></p></li>
</ul>
<p>Field是torchtext中定义数据类型以及转换为张量的指令。<code class="docutils literal notranslate"><span class="pre">torchtext</span></code> 认为一个样本是由多个字段（文本字段，标签字段）组成，不同的字段可能会有不同的处理方式，所以才会有 <code class="docutils literal notranslate"><span class="pre">Field</span></code> 抽象。定义Field对象是为了明确如何处理不同类型的数据，但具体的处理则是在Dataset中完成的。下面我们通过一个例子来简要说明一下Field的使用：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">tokenize</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">TEXT</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Field</span><span class="p">(</span><span class="n">sequential</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">tokenize</span><span class="o">=</span><span class="n">tokenize</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fix_length</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="n">LABEL</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Field</span><span class="p">(</span><span class="n">sequential</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">use_vocab</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<p>其中：</p>
<p>​	sequential设置数据是否是顺序表示的；</p>
<p>​	tokenize用于设置将字符串标记为顺序实例的函数</p>
<p>​	lower设置是否将字符串全部转为小写；</p>
<p>​	fix_length设置此字段所有实例都将填充到一个固定的长度，方便后续处理；</p>
<p>​	use_vocab设置是否引入Vocab object，如果为False，则需要保证之后输入field中的data都是numerical的</p>
<p>构建Field完成后就可以进一步构建dataset了：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchtext</span> <span class="kn">import</span> <span class="n">data</span>
<span class="k">def</span> <span class="nf">get_dataset</span><span class="p">(</span><span class="n">csv_data</span><span class="p">,</span> <span class="n">text_field</span><span class="p">,</span> <span class="n">label_field</span><span class="p">,</span> <span class="n">test</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="n">fields</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="c1"># we won&#39;t be needing the id, so we pass in None as the field</span>
                 <span class="p">(</span><span class="s2">&quot;comment_text&quot;</span><span class="p">,</span> <span class="n">text_field</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;toxic&quot;</span><span class="p">,</span> <span class="n">label_field</span><span class="p">)]</span>       
    <span class="n">examples</span> <span class="o">=</span> <span class="p">[]</span>

    <span class="k">if</span> <span class="n">test</span><span class="p">:</span>
        <span class="c1"># 如果为测试集，则不加载label</span>
        <span class="k">for</span> <span class="n">text</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">csv_data</span><span class="p">[</span><span class="s1">&#39;comment_text&#39;</span><span class="p">]):</span>
            <span class="n">examples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">Example</span><span class="o">.</span><span class="n">fromlist</span><span class="p">([</span><span class="kc">None</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">fields</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">text</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">csv_data</span><span class="p">[</span><span class="s1">&#39;comment_text&#39;</span><span class="p">],</span> <span class="n">csv_data</span><span class="p">[</span><span class="s1">&#39;toxic&#39;</span><span class="p">])):</span>
            <span class="n">examples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">Example</span><span class="o">.</span><span class="n">fromlist</span><span class="p">([</span><span class="kc">None</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="n">label</span><span class="p">],</span> <span class="n">fields</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">examples</span><span class="p">,</span> <span class="n">fields</span>
</pre></div>
</div>
<p>这里使用数据csv_data中有&quot;comment_text&quot;和&quot;toxic&quot;两列，分别对应text和label。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">train_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;train_toxic_comments.csv&#39;</span><span class="p">)</span>
<span class="n">valid_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;valid_toxic_comments.csv&#39;</span><span class="p">)</span>
<span class="n">test_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;test_toxic_comments.csv&quot;</span><span class="p">)</span>
<span class="n">TEXT</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Field</span><span class="p">(</span><span class="n">sequential</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">tokenize</span><span class="o">=</span><span class="n">tokenize</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">LABEL</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Field</span><span class="p">(</span><span class="n">sequential</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">use_vocab</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>

<span class="c1"># 得到构建Dataset所需的examples和fields</span>
<span class="n">train_examples</span><span class="p">,</span> <span class="n">train_fields</span> <span class="o">=</span> <span class="n">get_dataset</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">TEXT</span><span class="p">,</span> <span class="n">LABEL</span><span class="p">)</span>
<span class="n">valid_examples</span><span class="p">,</span> <span class="n">valid_fields</span> <span class="o">=</span> <span class="n">get_dataset</span><span class="p">(</span><span class="n">valid_data</span><span class="p">,</span> <span class="n">TEXT</span><span class="p">,</span> <span class="n">LABEL</span><span class="p">)</span>
<span class="n">test_examples</span><span class="p">,</span> <span class="n">test_fields</span> <span class="o">=</span> <span class="n">get_dataset</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">TEXT</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">test</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># 构建Dataset数据集</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">train_examples</span><span class="p">,</span> <span class="n">train_fields</span><span class="p">)</span>
<span class="n">valid</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">valid_examples</span><span class="p">,</span> <span class="n">valid_fields</span><span class="p">)</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">test_examples</span><span class="p">,</span> <span class="n">test_fields</span><span class="p">)</span>
</pre></div>
</div>
<p>可以看到，定义Field对象完成后，通过get_dataset函数可以读入数据的文本和标签，将二者（examples）连同field一起送到torchtext.data.Dataset类中，即可完成数据集的构建。使用以下命令可以看下读入的数据情况：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 检查keys是否正确</span>
<span class="nb">print</span><span class="p">(</span><span class="n">train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="c1"># 抽查内容是否正确</span>
<span class="nb">print</span><span class="p">(</span><span class="n">train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">comment_text</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p><strong>词汇表（vocab）</strong></p></li>
</ul>
<p>在NLP中，将字符串形式的词语（word）转变为数字形式的向量表示（embedding）是非常重要的一步，被称为Word Embedding。这一步的基本思想是收集一个比较大的语料库（尽量与所做的任务相关），在语料库中使用word2vec之类的方法构建词语到向量（或数字）的映射关系，之后将这一映射关系应用于当前的任务，将句子中的词语转为向量表示。</p>
<p>在torchtext中可以使用Field自带的build_vocab函数完成词汇表构建。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">TEXT</span><span class="o">.</span><span class="n">build_vocab</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p><strong>数据迭代器</strong></p></li>
</ul>
<p>其实就是torchtext中的DataLoader，看下代码就明白了：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchtext.data</span> <span class="kn">import</span> <span class="n">Iterator</span><span class="p">,</span> <span class="n">BucketIterator</span>
<span class="c1"># 若只针对训练集构造迭代器</span>
<span class="c1"># train_iter = data.BucketIterator(dataset=train, batch_size=8, shuffle=True, sort_within_batch=False, repeat=False)</span>

<span class="c1"># 同时对训练集和验证集进行迭代器的构建</span>
<span class="n">train_iter</span><span class="p">,</span> <span class="n">val_iter</span> <span class="o">=</span> <span class="n">BucketIterator</span><span class="o">.</span><span class="n">splits</span><span class="p">(</span>
        <span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">valid</span><span class="p">),</span> <span class="c1"># 构建数据集所需的数据集</span>
        <span class="n">batch_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span>
        <span class="n">device</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="c1"># 如果使用gpu，此处将-1更换为GPU的编号</span>
        <span class="n">sort_key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">comment_text</span><span class="p">),</span> <span class="c1"># the BucketIterator needs to be told what function it should use to group the data.</span>
        <span class="n">sort_within_batch</span><span class="o">=</span><span class="kc">False</span>
<span class="p">)</span>

<span class="n">test_iter</span> <span class="o">=</span> <span class="n">Iterator</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">device</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sort_within_batch</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<p>torchtext支持只对一个dataset和同时对多个dataset构建数据迭代器。</p>
<ul class="simple">
<li><p><strong>使用自带数据集</strong></p></li>
</ul>
<p>与torchvision类似，torchtext也提供若干常用的数据集方便快速进行算法测试。可以查看<a class="reference external" href="https://pytorch.org/text/stable/datasets.html">官方文档</a>寻找想要使用的数据集。</p>
</section>
<section id="metric">
<h2>8.4.4 评测指标（metric）<a class="headerlink" href="#metric" title="永久链接至标题">#</a></h2>
<p>NLP中部分任务的评测不是通过准确率等指标完成的，比如机器翻译任务常用BLEU (bilingual evaluation understudy) score来评价预测文本和标签文本之间的相似程度。torchtext中可以直接调用torchtext.data.metrics.bleu_score来快速实现BLEU，下面是一个官方文档中的一个例子：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchtext.data.metrics</span> <span class="kn">import</span> <span class="n">bleu_score</span>
<span class="n">candidate_corpus</span> <span class="o">=</span> <span class="p">[[</span><span class="s1">&#39;My&#39;</span><span class="p">,</span> <span class="s1">&#39;full&#39;</span><span class="p">,</span> <span class="s1">&#39;pytorch&#39;</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;Another&#39;</span><span class="p">,</span> <span class="s1">&#39;Sentence&#39;</span><span class="p">]]</span>
<span class="n">references_corpus</span> <span class="o">=</span> <span class="p">[[[</span><span class="s1">&#39;My&#39;</span><span class="p">,</span> <span class="s1">&#39;full&#39;</span><span class="p">,</span> <span class="s1">&#39;pytorch&#39;</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;Completely&#39;</span><span class="p">,</span> <span class="s1">&#39;Different&#39;</span><span class="p">]],</span> <span class="p">[[</span><span class="s1">&#39;No&#39;</span><span class="p">,</span> <span class="s1">&#39;Match&#39;</span><span class="p">]]]</span>
<span class="n">bleu_score</span><span class="p">(</span><span class="n">candidate_corpus</span><span class="p">,</span> <span class="n">references_corpus</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">0.8408964276313782</span>
</pre></div>
</div>
</section>
<section id="id4">
<h2>8.4.5 其他<a class="headerlink" href="#id4" title="永久链接至标题">#</a></h2>
<p>值得注意的是，由于NLP常用的网络结构比较固定，torchtext并不像torchvision那样提供一系列常用的网络结构。模型主要通过torch.nn中的模块来实现，比如torch.nn.LSTM、torch.nn.RNN等。</p>
<p><strong>备注：</strong></p>
<p>对于文本研究而言，当下Transformer已经成为了绝对的主流，因此PyTorch生态中的<a class="reference external" href="https://huggingface.co/">HuggingFace</a>等工具包也受到了越来越广泛的关注。这里强烈建议读者自行探索相关内容，可以写下自己对于HuggingFace的笔记，如果总结全面的话欢迎pull request，充实我们的课程内容。</p>
<p><strong>本节参考</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://pytorch.org/text/stable/index.html">torchtext官方文档</a></p></li>
<li><p><a class="reference external" href="https://github.com/atnlp/torchtext-summary">atnlp/torchtext-summary</a></p></li>
</ul>
</section>
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